Authors: M.S. Pradeep Kumar; Krishnappa Suresh
Addresses: Department of Telecommunication of Engineering, Don Bosco Institute of Technology, India ' Electronics and Communication, Sri Dharmasthal Institute of Technology, India
Abstract: Emotion recognition is an important field of research in brain-computer interactions. As technology and the understanding of emotions are advancing, there are growing opportunities for automatic emotion recognition systems. The brain uses the neuromuscular channels to communicate and control its external environment, however many disorders can disrupt these channels. An electroencephalogram (EEG) signals are generated in the human brain, communicate with several neurons and low amplitude signals. In this paper, the hybrid feature extraction (Renyi and differential entropy) was performed on the acquired EEG signal in order to achieve feature subsets. The respective feature values were given as the input for a multi-objective classifier: deep neural network (DNN) for classifying the disabled persons and their emotions. The proposed technique improves the emotion prediction accuracy for different sessions. The emotional recognition classification model includes three states: positive, neutral and negative. In experimental analysis, the proposed approach classifies the disabled persons and their emotions by means of specificity, sensitivity, and accuracy. The experimental outcome shows that the proposed methodology improved accuracy in emotion classification up to 8.01% compared to the existing methods: k-nearest neighbours (KNN) and support vector machine (SVM).
Keywords: deep neural network; DNN; electroencephalogram; EEG; differential entropy; emotion recognition; Renyi entropy.
International Journal of Advanced Intelligence Paradigms, 2021 Vol.19 No.3/4, pp.295 - 313
Received: 12 Feb 2018
Accepted: 31 Mar 2018
Published online: 09 Jul 2021 *